Results 41 to 50 of about 414,150 (267)

A Kernel-Based Metric for Balance Assessment

open access: yesJournal of Causal Inference, 2018
An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in ...
Zhu Yeying   +2 more
doaj   +1 more source

The variance of causal effect estimators for binary v-structures

open access: yesJournal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
doaj   +1 more source

Comment: Causal Inference in the Medical Area

open access: yes, 2006
Comment on Causal Inference in the Medical Area [math.ST/0612783]Comment: Published at http://dx.doi.org/10.1214/088342306000000286 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.
Korn, Edward L.
core   +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

open access: yesFEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

Prospective and retrospective causal inferences based on the potential outcome framework

open access: yesJournal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi   +4 more
doaj   +1 more source

Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction

open access: yesJournal of Causal Inference, 2019
Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional
Luo Wei, Wu Wenbo, Zhu Yeying
doaj   +1 more source

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

open access: yesJournal of Causal Inference, 2022
Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of ...
Jin Yutong, Benkeser David
doaj   +1 more source

A methionine‐lined active site governs carbocation stabilization and product specificity in a bacterial terpene synthase

open access: yesFEBS Letters, EarlyView.
This study reveals a unique active site enriched in methionine residues and demonstrates that these residues play a critical role by stabilizing carbocation intermediates through novel sulfur–cation interactions. Structure‐guided mutagenesis further revealed variants with significantly altered product profiles, enhancing pseudopterosin formation. These
Marion Ringel   +13 more
wiley   +1 more source

Sufficient Causes: On Oxygen, Matches, and Fires

open access: yesJournal of Causal Inference, 2019
We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments.
Pearl Judea
doaj   +1 more source

Decision-theoretic foundations for statistical causality

open access: yesJournal of Causal Inference, 2021
We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions.
Dawid Philip
doaj   +1 more source

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